Optical coherence tomography (OCT) is a noninvasive, noncontact imaging modality that uses coherence gating to obtain high-resolution cross-sectional images of tissue microstructure. Several implementations of OCT have been developed. In Frequency domain OCT (FD-OCT), the interferometric signal between light from a reference and the back-scattered light from a sample point is recorded in the frequency domain either by using a dispersive spectrometer in the detection arm in the case of spectral-domain OCT (SD-OCT) or rapidly tuning a swept laser source in the case of swept-source OCT (SS-OCT). After a wavelength calibration, a one-dimensional Fourier transform is taken to obtain an A-line spatial distribution of the object scattering potential.
Functional OCT can provide important clinical information that is not available in the typical intensity based structural OCT images. There have been several functional contrast enhancement methods including Doppler OCT, Phase-sensitive OCT measurements, Polarization Sensitive OCT, Spectroscopic OCT, etc. Integration of functional extensions can greatly enhance the capabilities of OCT for a range of applications in medicine.
One of the most promising functional extensions of OCT has been the field of OCT angiography which is based on flow contrast. Visualization of the detailed vasculature using OCT could enable doctors to obtain new and useful clinical information for diagnosis and management of eye diseases in a non-invasive manner. Fluorescein angiography and indocyanine green (ICG) angiography are currently the gold standards for vasculature visualization in the eye. However, the invasiveness of these approaches combined with possible complications (allergy to dyes, side effects) make them unsuitable techniques for widespread screening applications in ophthalmic clinics. There are several flow contrast techniques in OCT imaging that utilize the change in data between successive B-scans or frames (inter-frame change analysis) of the OCT intensity or phase-resolved OCT data. One of the major applications of such techniques has been to generate en face vasculature images of the retina. High resolution en face visualization based on inter-frame change analysis requires high density of sampling points and hence the time required to finish such scans can be up to an order of magnitude higher compared to regular cube scans used in commercial OCT systems.
While OCT angiography appears to be an exciting technology, there are several technical limitations that need to be overcome before it can gain widespread acceptance in clinical settings. Typically, the most common approach for determining motion contrast is to obtain multiple B-scans (at the same location or closely spaced) and analyze the change in OCT data due to motion. One of the major limitations of OCT angiography is the long acquisition times and associated motion artifacts that can affect analysis. Eye motion can result in loss of data, image artifacts and hence greatly reduces the usability of the acquired data. While axial motion can be detected and compensated for, it is relatively difficult and time consuming to detect all cases of transverse motion using post-processing methods alone. Since the algorithm derives signal from the change in OCT data, even small shifts in gaze or saccadic motion of the eye could result in significant artifacts. Post-processing methods to correct for transverse motion artifacts have limited success and are often very time consuming. One of the approaches to solve this problem is to use very high speed OCT systems, however, such systems can be very complex and costly (see for example T. Klein et al., “The effect of micro-saccades on the image quality of ultrawide-field multimegahertz OCT data,” SPIE Photonices West 2012, Paper #8209-13 (2012)).
Another challenge for the OCT angiography technology is to obtain retinal vasculature maps at large fields of view (FOV). The large acquisition times and huge data volumes make it impractical to obtain high resolution data over large FOVs. Acquisition of multiple smaller data cubes of smaller FOV and montaging them together using post-processing is one of the approaches that can be applied to work around this problem. Rosenfeld et al. recently demonstrated a method for automated montaging of SD-OCT data sets to generate images and analysis over larger FOV (see for example Y. Li et al., “Automatic montage of SD-OCT data sets,”, Optics Express, 19, 26239-26248 (2011)). However, their method relies on post-processing registration and alignment of multiple OCT cubes based on their OCT-fundus images. There are several limitations in this method. Sufficient overlap of the scanned data is required for optimized performance of the algorithms and it must be ensures that changes in gaze do not result in missing un-scanned regions on the retina. Also, if there is some motion during the scan, it cannot be corrected using this method.
In light of the limitations in the prior art, a need exists to obtain motion artifact free OCT angiography images, especially large field of view images.